Prescription medication use in persons many years following traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: This research study examined the prevalence of prescription medication use in persons many years following moderate-to-severe traumatic brain injury (TBI). DESIGN: Retrospective cohort study. SETTING AND SUBJECTS: Consecutive records were examined of persons with moderate-to-severe TBI who were discharged from a large rehabilitation hospital in Pennsylvania from 1973-1989. Consenting participants (n = 306) were interviewed, who were traced up to 24 years post-injury. Data on current use of prescription medications, in addition to demographic characteristics and health conditions were collected from the participants. RESULTS: The prevalence of prescription medication was 58.9% in the sample, greater in females (65.6%) than in males (56.1%). The most prescribed medication types were anti-convulsants (25.8%) followed by anti-depressants (8.2%), painkillers (8.2%) and anti-anxiety medications (5.9%). On average, persons with TBI were prescribed 2.64 (SD = 2.14) medications with a range of 1-12. CONCLUSION: The research findings indicate a high prevalence of prescription medications in persons with past history of TBI. There is also a high prevalence of anti-convulsants medication use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".